提出CAJO角色框架，让推荐系统更好帮群体做决策
Widening the Role of Group Recommender Systems with CAJO
- 定义CAJO角色框架，重新定位群体推荐系统作用
- 指出当前群体推荐系统因缺乏协作机制而发展滞后
- 适合研究人机协同与群体决策的学者参考
群体推荐系统（GRSs）已研究超过二十年，但实际应用并未普及，甚至可视为失败，相较之下主流电商与社交平台普遍使用的个体推荐系统已取得巨大成功。当前系统仅服务于个人用户，无法为群体共同选择产品、服务或体验提供支持，难以兼顾所有成员需求。本文通过一篇观点文章，分析群体推荐系统发展滞后的根本原因，并提出一项研究计划，聚焦于人类与智能系统间新型协作形式的探索。为此，我们定义了一组新角色——CAJO，旨在使群体推荐系统在群体决策中发挥更有效的作用。
原文摘要 · Abstract (English)
Group Recommender Systems (GRSs) have been studied and developed for more than twenty years. However, their application and usage has not grown. They can even be labeled as failures, if compared to the very successful and common recommender systems (RSs) used on all the major ecommerce and social platforms. As a result, the RSs that we all use now, are only targeted for individual users, aiming at choosing an item exclusively for themselves; no choice support is provided to groups trying to select a service, a product, an experience, a person, serving equally well all the group members. In this opinion article we discuss why the success of group recommender systems is lagging and we propose a research program unfolding on the analysis and development of new forms of collaboration between humans and intelligent systems. We define a set of roles, named CAJO, that GRSs should play in order to become more useful tools for group decision making.
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